Concurrency in Electrical Neuroinformatics: Parallel Computation for Studying the Volume Conduction of Brain Electrical Fields in Human Head Tissues
Publication Type
Original research
  • Adnan Salman
  • Allen Malony
  • Sergei Turovets
  • Vasily Volkov
  • David Ozog
  • Don Tucker

Advances in human brain neuroimaging for high-temporal and high-spatial resolution will depend ‎on localization of Electroencephalography (EEG) signals to their cortex sources. The source ‎localization inverse problem is inherently ill-posed and depends critically on the modeling of human ‎head electromagnetics. We present a systematic methodology to analyze the main factors and ‎parameters that affect the EEG source-mapping accuracy. These factors are not independent and ‎their effect must be evaluated in a unified way. To do so requires significant computational ‎capabilities to explore the problem landscape, quantify uncertainty effects, and evaluate ‎alternative algorithms. Bringing high-performance computing (HPC) to this domain is necessary to ‎open new avenues for neuroinformatics research. The head electromagnetics forward problem is ‎the heart of the source localization inverse. We present two parallel algorithms to address tissue ‎inhomogeneity and impedance anisotropy. Highly-accurate head modeling environments will ‎enable new research and clinical neuroimaging applications. Cortex-localized dEEG analysis is the ‎next-step in neuroimaging domains such as early childhood reading, understanding of resting state ‎brain networks, and models of full brain function. Therapeutic treatments based on ‎neurostimulation will also depend significantly on HPC integration.‎

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